POV: Chinese AI Lab Teaching Everyone How To Save Millions of Dollars

ByteDance's AI lab has published research on Pre-trained Model Averaging (PMA), a technique that merges model checkpoints during training to predict final performance while saving 15% of compute budget. The method averages snapshots taken at fixed intervals during the constant learning rate phase, effectively achieving similar results to traditional annealing without the computational cost. Testing on models from 411M to 70B parameters showed 3-7% accuracy gains and potential savings of millions in training costs. The technique also provides crash recovery capabilities and early performance estimates for hyperparameter optimization.